Agile software development has evolved into an increasingly mature software development approach and has been applied successfully in many software vendors’ development departments. In this position paper, we address the broader agile service development. Based on method engineering principles we define a framework that conceptualizes an operational way of working for the development of services, emphatically taking into account agility. As a first level of agility, the framework contains situational project factors that influence the choice of method fragments; secondly, increased agility is proposed by describing and operationalizing these method fragments not as imperative steps or activities, but instead by means of sets of minimally specified, declarative rules that determine the context and constraints within which goals are to be reached. This approach borrows concepts from rules management, organizational patterns, and game design theory. Keywordsmethod engineering–agile service development–business rules–business rules management–product management–game design
The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
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Update 09 22: de generieke blackboardmodule is afgerond in juni 22 en wordt nu bij Bedrijfskunde (AD) en Technische Bedrijfskunde (Minor Slim Produceren) gebruikt en getest. Deze gebruikerservaringen worden meegenomen, mogelijke aanpassingen worden gedaan. Het project loopt volgens planning. Er blijkt geen factuur te zijn gestuurd in het vorig studiejaar (21 22), dit blijkt niet meer mogelijk. Voor dit project is subsidie uit het Strategisch Fonds toegekend door drie instituten: Instituut voor Bedrijfskunde, Academie voor Gezondheidsstudies en Engineering. Looptijd: september 2021 - juni 2023.Er wordt een generieke Blackboard module ontwikkeld voor het gebruik van de A3+ methode door studenten (gebruik) en docenten (begeleiding). Het is een onderzoeksmethode om procesproblemen te analyseren en op te lossen op een iteratieve en sequentiële wijze. De beoogde impact: Met behulp van de onlineversie van de A3+ methode wordt een nieuwe structuur gecreëerd voor de begeleiding van studenten bij de uitvoering van hun onderzoeksopdrachten. Kenmerk van deze nieuwe structuur is haar flexibele karakter met hoogfrequente online interactie en feedback
The pace of technology advancements continues to accelerate, and impacts the nature of systems solutions along with significant effects on involved stakeholders and society. Design and engineering practices with tools and perspectives, need therefore to evolve in accordance to the developments that complex, sociotechnical innovation challenges pose. There is a need for engineers and designers that can utilize fitting methods and tools to fulfill the role of a changemaker. Recognized successful practices include interdisciplinary methods that allow for effective and better contextualized participatory design approaches. However, preliminary research identified challenges in understanding what makes a specific method effective and successfully contextualized in practice, and what key competences are needed for involved designers and engineers to understand and adopt these interdisciplinary methods. In this proposal, case study research is proposed with practitioners to gain insight into what are the key enabling factors for effective interdisciplinary participatory design methods and tools in the specific context of sociotechnical innovation. The involved companies are operating at the intersection between design, technology and societal impact, employing experts who can be considered changemakers, since they are in the lead of creative processes that bring together diverse groups of stakeholders in the process of sociotechnical innovation. A methodology will be developed to capture best practices and understand what makes the deployed methods effective. This methodology and a set of design guidelines for effective interdisciplinary participatory design will be delivered. In turn this will serve as a starting point for a larger design science research project, in which an educational toolkit for effective participatory design for socio-technical innovation will be designed.
To treat microbial infections, antibiotics are life-saving but the increasing antimicrobial resistance is a World-wide problem. Therefore, there is a great need for novel antimicrobial substances. Fruit and flower anthocyanins have been recognized as promising alternatives to traditional antibiotics. How-ever, for future application as innovative alternative antibiotics, the full potential of anthocyanins should be further investigated. The antimicrobial potential of anthocyanin mixtures against different bacterial species has been demonstrated in literature. Preliminary experiments performed by our laboratories, using grape, rose and red cabbage anthocyanins against S. aureus and E. coli confirmed the antimicrobial potential of these substances. Hundreds of different anthocyanin entities have been described. However, which of these entities hold antimicrobial effects is currently unknown. Our preliminary data show that an-thocyanins extracted from grape, rose and red cabbage contain different collections of anthocyanin entities with differential antimicrobial efficacies. Our focus is on the extraction and characterization of anthocyanins from various crop residues. Grape peels are residues in the production of wine, while red rose and tulip leaves are residues in the production of tulip bulbs and regular horticulture. The presence of high-grade substances for pharmacological purposes in these crops may provide an innovative strategy to add value to other-wise invaluable crop residues. This project will be performed by the collaborative effort of our institute together with the Medi-cal Microbiology department of the University Medical Center Groningen (UMCG), 'Wijnstaete', a small-scale wine-producer (Lemelerveld) and Imenz Bioengineering (Groningen), a company that develops processes to improve the production of biobased chemicals from waste products. Within this project, we will focus on the antimicrobial efficacy of anthocyanin-mixtures from sources that are abundantly and locally available as a residual waste product. The project is part of a larger re-search effect to further characterize, modify and study the antimicrobial effects of specific anthocy-anin entities.
